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UID:pretalx-nss-convention-2026-JSAP8C@talks.caving.dev
DTSTART;TZID=EST:20260710T104000
DTEND;TZID=EST:20260710T110000
DESCRIPTION:Sinkholes are a defining feature of karst landscapes that play 
 a critical role in hazard management and groundwater quality. In the Unite
 d States\, sinkhole damages are estimated to cost at least $300 million an
 nually. They can also act as recharge points and contaminant pathways into
  karst aquifers\, allowing for surface contaminants to enter groundwater. 
 Recent advancements in geospatial analysis have improved the ability to de
 tect and map sinkholes across large areas. High-resolution digital elevati
 on models (DEM) derived from LiDAR data are used to calculate the morphome
 try and spatial distribution of detected sinkhole features. Sinkhole morph
 ometry measurements can be used to understand the evolution and dissolutio
 n processes that shape karst landscapes.\n\nThis study evaluates how diffe
 rent mapping approaches influence sinkhole morphometry across diverse envi
 ronmental settings. We compared two nationwide sinkhole datasets for the U
 nited States that were developed using fundamentally different methods. Th
 e USGS dataset uses a flow routing algorithm\, while the Mihevc dataset us
 es a machine learning algorithm. These approaches also differ in DEM resol
 ution\, processing tools\, parameter thresholds\, and artifact removal pro
 cedures. We used QGIS and Python to quantify and compare morphometric para
 meters within a range of karst settings. Sinkhole polygons were smoothed t
 o reduce pixelation effects associated with DEM resolution when calculatin
 g parameters like circularity. Differences in sinkhole size\, shape\, and 
 spatial distribution help identify strengths and limitations related to ea
 ch method.
DTSTAMP:20260826T232053Z
LOCATION:Auditorium
SUMMARY:Quantifying and Comparing Sinkhole Morphometry across Different Geo
 spatial Datasets - 
URL:https://talks.caving.dev/nss-convention-2026/talk/JSAP8C/
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